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Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
Vericom: A Verification and Communication Architecture fo...
Ali Dorri, Shailesh Mishra, Raja Jurdak · 2021-05-26 · via cs.CR updates on arXiv.org

Blockchain has received tremendous attention as a secure, distributed, and anonymous framework for the Internet of Things (IoT). As a distributed system, blockchain trades off scalability for distribution, which limits the technologys adaptation for large scale networks such as IoT. All transactions and blocks must be broadcast and verified by all participants which limits scalability and incurs computational and communication overheads. The existing solutions to scale blockchains have so far led to partial recentralization, limiting the technologys original appeal. In this paper, we introduce a distributed yet scalable Verification and Communication architecture for blockchain referred to as Vericom. Vericom concurrently achieves high scalability and distribution using hash function outputs to shift blockchains from broadcast to multicast communication. Unlike conventional blockchains where all nodes must verify new transactions/blocks, Vericom uses the hash of IoT traffic to randomly select a set of nodes to verify transactions/blocks which in turn reduces the processing overhead. Vericom incorporates two layers: i) transmission layer where a randomized multicasting method is introduced along with a backbone network to route traffic, i.e., transactions and blocks, from the source to the destination, and ii) verification layer where a set of randomly selected nodes are allocated to verify each transaction or block. The performance evaluation shows that Vericom reduces the packet and processing overhead as compared with conventional blockchains. In the worst case, packet overhead in Vericom scales linearly with the number of nodes while the processing overhead remains scale-independent.